AI Integration

One feature, deliberately. This package takes a single use case, an assistant, RAG search or one automation, from a prioritised idea to something running in production with guardrails, rather than an open-ended "add AI" programme. We start with a short discovery to score the use cases on value, feasibility and risk, agree the one to build first and keep the rest as a backlog. If a broader rollout makes sense later, we phase it, but we'd rather ship one feature your users rely on than five that only demo well.

Usually, yes. We start by reviewing what you have: the prompts, the retrieval setup and where it breaks, and keep whatever earns its place. The gap with most prototypes isn't the happy path; it's evaluation, guardrails, monitoring and a human fallback for when the model is unsure, which is exactly what this package adds. If the foundation is sound we build on it; if it'll fight us past week two, we'll tell you honestly and rebuild the part that needs it.

We ship with monitoring and an eval suite, so accuracy is measured in production, not assumed. When answer quality drifts or a provider changes a model, you see it instead of finding out from a user. You own that eval suite and the retrieval index over your data, so your team can re-run the checks and tune prompts without us. Where you'd rather we stayed on, we can keep the feature maintained on a light retainer; either way, it's handed over with runbooks and the limits written down.

We default to retrieval over your own data, so answers are grounded in your sources and cite where they came from instead of being generated from the model's memory. Before launch we build evals on real cases with acceptance thresholds, so we measure answer quality rather than hoping it's good, and we add input and output guardrails plus a human-owned fallback for when the model is unsure. Where we can't reach a quality bar we can stand behind, we'll tell you the feature isn't ready rather than ship a confident guess.

By default, no. We pseudonymise or abstract where needed and follow per-client rules about what may or may not leave your environment. For clients with stricter requirements, we can use self-hosted or tenant-isolated AI options, defined together during onboarding.

No lock-in by design. We treat the model as a swappable component behind your own retrieval layer and structured-output contracts, and we work across providers like OpenAI, Anthropic, Google Gemini, Azure OpenAI and AWS Bedrock so you can move if pricing, latency or policy changes. You own the same things you own everywhere else with us: the code, the prompts, the eval suite and the retrieval index over your data, handed over with documentation, so the feature isn't dependent on us or on a single vendor to keep running.